MétaCan
Menu
Back to cohort
Record W2319505397 · doi:10.1149/1.2911520

Silicides for 22nm and Beyond

2008· article· en· W2319505397 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Transactions · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsAdvanced Micro Devices (Canada)
FundersBasic Energy SciencesOffice of ScienceU.S. Department of Energy
KeywordsSilicideMaterials scienceArchitectureEngineering physicsScalingCMOSNanotechnologyStability (learning theory)Computer architectureComputer scienceOptoelectronicsElectronic engineeringSiliconEngineeringMathematicsGeography

Abstract

fetched live from OpenAlex

Silicide engineering for high-performance CMOS logic devices is challenged by aggressive scaling of critical dimensions, new high-performance elements, and requirements of morphological stability. While NiSi can satisfy many of the integration challenges, incorporating Pt forms a more robust [NixPt(1-x)]Si and improves morphological stability. In light of the challenges created by performance enablers, we review our latest results indicating whether a replacement for NiPt(1-x)Si is needed and highlight our investigations into alternative silicide materials such as Er, Yb, and Ir. We also discuss the architecture and performance needs beyond 32nm technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.243
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it